Periodic sensor networks (PSNs) represent one of the important networks which are used in Internet of Things (IoTs) future. It has gained a rapid increased interest due to its wide use in various fields of applications. However, it suffers from the drawback of a limited lifetime as its power supply cannot be replaced or renew due to the nature of its deployment. Much of the research in this area has been focusing on conserving energy in whatever way possible. In this study, we tackled the issue of reducing the data volume transmitted to the final base station or sink. We proposed an energy-efficient two-layer data transmission reduction scheme that exploits both temporal and spatial redundancy in sensed data. The first layer is based on a modified version of the kNN algorithm and is applied at the sensor node level. Bottom-up hierarchical clustering is implemented at the cluster head or aggregator to reduce data received by aggregator before sending it to the sink node. The proposed approach in layer 2 exploits the spatial correlation between neighboring nodes to remove the redundancy of data and thus to improve the lifetime of the PSN. The proposed approach is implemented using OMNeT++ network simulator using real sensed data from Intel Berkeley Lab sensors. A number of experiments have been conducted to evaluate the proposed approach. The results that are compared with some recently published methods show that the proposed approach outperforms the other methods in terms of many performance metrics such as data reduction rate, energy consumption, and lost data.

Energy-efficient two-layer data transmission reduction protocol in periodic sensor networks of IoTs

Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques: - lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like- file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.

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Performance analysis of compression algorithms for information security: A Review

There is an increasing number of image data produced in our life nowadays, which creates a big challenge to store and transmit them. For some fields requiring high fidelity, the lossless image compression becomes significant, because it can reduce the size of image data without quality loss. To solve the difficulty in improving the lossless image compression ratio, we propose an improved lossless image compression algorithm that theoretically provides an approximately quadruple compression combining the linear prediction, integer wavelet transform (IWT) with output coefficients processing and Huffman coding. A new hybrid transform exploiting a new prediction template and a coefficient processing of IWT is the main contribution of this algorithm. The experimental results on three different image sets show that the proposed algorithm outperforms state-of-the-art algorithms. The compression ratios are improved by at least 6.22% up to 72.36%. Our algorithm is more suitable to compress images with complex texture and higher resolution at an acceptable compression speed.

An improved lossless image compression algorithm based on Huffman coding

This paper discusses compression methods focused on data transmission over communication channels. The characteristics of different algorithms for different types of incoming data are analyzed. The purpose of this study is to evaluate the speed of operation of each of the compression algorithms for different types of information and different compression parameters, on the basis of the obtained results to make recommendations for the application of compression methods in systems critical to the performance of the algorithm. Based on the results of the analysis, the methods of compression that can be used in communication channels are selected: LZW, LZH, Vitter and matrix. The practical research of the selected methods on different information flows (text, graphics, measurement data, combined data) was carried out, their comparative analysis was performed. Research has highlighted compression methods that give the most optimal results in each case. Comparative evaluation of algorithms for different parameters is made, the possibility of data compression implementation in systems running in real time is analyzed. Based on the results of the study, recommendations are made for the application of particular compression methods in specific conditions.

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Increasing the efficiency of information transmission in communication channels

The development of information technology has brought an avoidable consequence for data security. It is because data, specifically those are private or confidential, are vulnerable to illegal access. Therefore, a protection method, such as data hiding, is essential. This protection is done by inserting a secret message (payload) into a medium (cover). One of the media commonly used in this method is audio, where a payload is embedded in audio samples. Nevertheless, this common technique has several disadvantages, including the relatively small insertion space and the significant dissimilarity between stego-audio and its original audio cover. Moreover, there is a risk of payload that cannot be returned to the original file. To overcome these problems, we propose to employ the fuzzification stage in the fuzzy logic algorithm that the samples are grouped into five categories. The binary payload is evenly distributed to the available sample space, which previously was calculated by considering the total payload and the number of interpolated samples. In the case that space is not enough to carry the payload, it is dynamically increased. The results show an increase of about 48% in stego audio quality measured by the Peak Signal-to-Noise Ratio (PSNR). Moreover, the method is reversible, and the size of the sample space can be controlled to specific values.

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Utilizing Fuzzy Logic in Developing Reversible Data Hiding Method

This paper provides the study of various lossless data compression techniques and compares their performance and efficiency using time and space complexity. Data compression is itself a huge field in a computer science and it is used in reducing the size of data by removing the redundant characters or encoding the bits in data. We have used only lossless data compression techniques for compressing various media format files like text, doc, bmp, png and wav. Huffman coding which generates variable length codes for each symbol. LZW is a dictionary based technique and Shannon-Fano coding is used to generate binary code based on the frequency of each symbol. So we calculated the mean compression ratio, compression factor and compression time for various media files and result analysis of above data compression techniques will help to select the most appropriate method of compressing of any given file format in real world.

Lossless data compression techniques and their performance

This paper proposes a new compression technique and compares its performance with the popular adaptive Huffman encoding. The main objective of our approach was to shrink the original data optimally without losing data integrity. The above proposed algorithm employs static variable length non prefix codes for ASCII characters that have been assigned by analyzing the character occurring probabilities in variables lossless data file. We have used several lossless data formats like BMP, PNG, TXT, WAV, FLAC, AAC, WMV, DOC. For comparison and analysis we have used compression ratio, compression time, compression factor and saving percentage. A windows executable application has been constructed to facilitate and ease application usage. Moreover the addition of encryption algorithm(Caesar Cipher) will ensure the file protection and privacy. This compression can easily handle large files and without consuming much of system's resources.

Komal Sharma

Papers

Lossless data compression techniques and their performance

Lossless data compression technique with encryption based approach